I suggest you think very carefully about the problem you want to solve. I have found Pinheiro and Bates (2000) Mixed-Effects Models in S and S-Plus (Springer) quite helpful both in theory and in how to implement it. Bates is the primary architect of lme, nlme, etc., and this book provides basic documentation for that package. In particular, I would expect that "simulate.lme" could be quite useful if you have any doubts about any issue.
hope this helps. spencer graves
Dimitris Rizopoulos wrote:
Hi Steve,
Estimation problems for the variance components in linear mixed models are usually occur for two reasons:
1. Due to model misspecification, i.e., using years instead of decades may show no variability in the slopes
2. Because the data do not support the assumptions of the linear mixed model (i.e., positive definite covariance matrix for the random-effects => increasing variance with time).
These may cause zero or even negative variance components. For more info you could take a look at Verbeke and Molenberghs (2000, Section 5.6) and Searle, Casella and McCullogh (1992, Section 3.5).
I don't know the exact formulation you are using, but maybe you could consider an analogue of you model using "gls", i.e.,
lme(..., random=~1|id) gls(..., corr=corCompSymm(form=~1|id))
The references mentioned above are:
@Book{verbeke.molenberghs:00, author = {G. Verbeke and G. Molenberghs}, title = {Linear Mixed Models for Longitudinal Data}, year = {2000}, address = {New York}, publisher = {Springer-Verlag} }
@Book{searle.et.al:92, author = {S. Searle and G. Cassela and C. McCulloch}, title = {Variance Components}, year = {1992}, address = {New York}, publisher = {Wiley} }
I hope it helps.
Best, Dimitris
---- Dimitris Rizopoulos Ph.D. Student Biostatistical Centre School of Public Health Catholic University of Leuven
Address: Kapucijnenvoer 35, Leuven, Belgium Tel: +32/16/396887 Fax: +32/16/337015 Web: http://www.med.kuleuven.ac.be/biostat/ http://www.student.kuleuven.ac.be/~m0390867/dimitris.htm
----- Original Message ----- From: "Steve Roberts" <[EMAIL PROTECTED]>
To: <[EMAIL PROTECTED]>
Sent: Tuesday, September 21, 2004 1:38 PM
Subject: [R] lme RE variance computation
As I understand it lme (in R v1.9.x) estimates random effect variances on a log scale, constraining them to be positive. Whilst this seems sensible, it does lead to apparently biased estimates if the variance is actually zero - which makes our simulation results look strange. Whilst we need to think a bit deeper about it - I still haven't got my head around what a negative variance could mean - does anyone know a way to take away the contraint and allowing zero or negative variances?
Steve. Dr Steve Roberts [EMAIL PROTECTED]
Senior Lecturer in Medical Statistics, CMMCH NHS Trust and University of Manchester Biostatistics Group, 0161 275 5192 / 0161 276 5785
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